Analysis of GLDS-218 from NASA GeneLab
This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven Xijin.Ge@sdstate.edu
Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491
First we set up the working directory to where the files are saved.
setwd('~/Documents/HTML_R/GLDS218')
R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.
if(file.exists('iDEP_core_functions.R'))
source('iDEP_core_functions.R') else
source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')
We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).
inputFile <- 'GLDS218_Expression.csv'
sampleInfoFile <- 'GLDS218_Sampleinfo.csv'
gldsMetadataFile <- 'GLDS218_Metadata.csv'
geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc.
geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db' # pathway database in SQL; can be GMT format
STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'
Parameters for reading data
input_missingValue <- 'geneMedian' #Missing values imputation method
input_dataFileFormat <- 1 #1- read counts, 2 FKPM/RPKM or DNA microarray
input_minCounts <- 0.5 #Min counts
input_NminSamples <- 1 #Minimum number of samples
input_countsLogStart <- 4 #Pseudo count for log CPM
input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr) # install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| AthaWsleafFLTRep1RNAseqRNAseq | AthaWsleafFLTRep2RNAseqRNAseq | AthaWsleafFLTRep3RNAseqRNAseq | AthaWsleafGCRep1RNAseqRNAseq | AthaWsleafGCRep2RNAseqRNAseq | AthaWsleafGCRep3RNAseqRNAseq | AthaWsrootFLTRep1RNAseqRNAseq | AthaWsrootFLTRep2RNAseqRNAseq | AthaWsrootFLTRep3RNAseqRNAseq | AthaWsrootGCRep1RNAseqRNAseq | AthaWsrootGCRep2RNAseqRNAseq | AthaWsrootGCRep3RNAseqRNAseq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X | Atha.Ws.leaf.FLT.Rep1 | Atha.Ws.leaf.FLT.Rep2 | Atha.Ws.leaf.FLT.Rep3 | Atha.Ws.leaf.GC.Rep1 | Atha.Ws.leaf.GC.Rep2 | Atha.Ws.leaf.GC.Rep3 | Atha.Ws.root.FLT.Rep1 | Atha.Ws.root.FLT.Rep2 | Atha.Ws.root.FLT.Rep3 | Atha.Ws.root.GC.Rep1 | Atha.Ws.root.GC.Rep2 | Atha.Ws.root.GC.Rep3 |
| Sample.Name | Atha_Ws_leaf_FLT_Rep1 | Atha_Ws_leaf_FLT_Rep2 | Atha_Ws_leaf_FLT_Rep3 | Atha_Ws_leaf_GC_Rep1 | Atha_Ws_leaf_GC_Rep2 | Atha_Ws_leaf_GC_Rep3 | Atha_Ws_root_FLT_Rep1 | Atha_Ws_root_FLT_Rep2 | Atha_Ws_root_FLT_Rep3 | Atha_Ws_root_GC_Rep1 | Atha_Ws_root_GC_Rep2 | Atha_Ws_root_GC_Rep3 |
| GLDS | 218 | 218 | 218 | 218 | 218 | 218 | 218 | 218 | 218 | 218 | 218 | 218 |
| Accession | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 | GLDS-218 |
| Hardware | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish | VEGGIE: Petri dish |
| Tissue | Shoots | Shoots | Shoots | Shoots | Shoots | Shoots | Roots | Roots | Roots | Roots | Roots | Roots |
| Age | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days |
| Organism | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana |
| Ecotype | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 |
| Genotype | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT |
| Variety | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT |
| Radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth |
| Gravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial |
| Developmental | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots |
| Time.series.or.Concentration.gradient | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point |
| Light | Light | Light | Light | Light | Light | Light | Light | Light | Light | Light | Light | Light |
| Assay..RNAseq. | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling | RNAseq Transcription Profiling & methylation profiling |
| Temperature | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 | 22-24 |
| Treatment.type | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana | Epigenomics in an extraterrestrial environment: organ-specific alteration of DNA methylation and gene expression elicited by spaceflight in Arabidopsis thaliana |
| Treatment.intensity | x | x | x | x | x | x | x | x | x | x | x | x |
| Treament.timing | x | x | x | x | x | x | x | x | x | x | x | x |
| Preservation.Method. | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater |
readData.out <- readData(inputFile)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
kable( head(readData.out$data) ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| leaf_FLT_Rep1 | leaf_FLT_Rep2 | leaf_FLT_Rep3 | leaf_GC_Rep1 | leaf_GC_Rep2 | leaf_GC_Rep3 | root_FLT_Rep1 | root_FLT_Rep2 | root_FLT_Rep3 | root_GC_Rep1 | root_GC_Rep2 | root_GC_Rep3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AT2G39730 | 17.78293 | 18.17313 | 18.16606 | 10.717916 | 11.207033 | 10.074624 | 18.40095 | 17.71456 | 17.77562 | 10.018001 | 10.011747 | 10.495878 |
| AT5G38410 | 17.37619 | 17.61313 | 17.61001 | 8.494508 | 9.108077 | 7.906913 | 17.83506 | 17.43857 | 17.46351 | 7.668614 | 8.160298 | 8.434496 |
| AT2G34420 | 15.79206 | 17.26853 | 17.10717 | 11.446016 | 11.911281 | 10.679680 | 17.10007 | 17.46595 | 17.29179 | 10.615707 | 10.424001 | 10.360353 |
| AT1G29910 | 15.55316 | 16.85377 | 16.79474 | 10.222624 | 10.860985 | 9.602112 | 16.87975 | 17.25823 | 17.06765 | 9.330598 | 9.289041 | 9.214425 |
| AT3G09260 | 11.07122 | 11.10264 | 10.55219 | 16.954220 | 17.024522 | 16.568068 | 10.03219 | 10.36804 | 10.20239 | 16.758730 | 16.893935 | 16.743435 |
| AT3G47470 | 15.62282 | 16.76636 | 16.77697 | 10.653710 | 11.075213 | 10.108569 | 16.94608 | 17.08041 | 16.93297 | 9.961969 | 10.041860 | 10.037081 |
readSampleInfo.out <- readSampleInfo(sampleInfoFile)
kable( readSampleInfo.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Gravity | Tissue | |
|---|---|---|
| leaf_FLT_Rep1 | Microgravity | Shoots |
| leaf_FLT_Rep2 | Microgravity | Shoots |
| leaf_FLT_Rep3 | Microgravity | Shoots |
| leaf_GC_Rep1 | Terrestrial | Shoots |
| leaf_GC_Rep2 | Terrestrial | Shoots |
| leaf_GC_Rep3 | Terrestrial | Shoots |
| root_FLT_Rep1 | Microgravity | Roots |
| root_FLT_Rep2 | Microgravity | Roots |
| root_FLT_Rep3 | Microgravity | Roots |
| root_GC_Rep1 | Terrestrial | Roots |
| root_GC_Rep2 | Terrestrial | Roots |
| root_GC_Rep3 | Terrestrial | Roots |
input_selectOrg ="NEW"
input_selectGO <- 'GOBP' #Gene set category
input_noIDConversion = TRUE
allGeneInfo.out <- geneInfo(geneInfoFile)
converted.out = NULL
convertedData.out <- convertedData()
nGenesFilter()
## [1] "16156 genes in 12 samples. 16074 genes passed filter.\n Original gene IDs used."
convertedCounts.out <- convertedCounts() # converted counts, just for compatibility
# Read counts per library
parDefault = par()
par(mar=c(12,4,2,2))
# barplot of total read counts
x <- readData.out$rawCounts
groups = as.factor( detectGroups(colnames(x ) ) )
if(nlevels(groups)<=1 | nlevels(groups) >20 )
col1 = 'green' else
col1 = rainbow(nlevels(groups))[ groups ]
barplot( colSums(x)/1e6,
col=col1,las=3, main="Total read counts (millions)")
readCountsBias() # detecting bias in sequencing depth
## [1] 4.020064e-06
## [1] 4.727448e-08
## [1] 0.8010792
## [1] "Warning! Sequencing depth bias detected. Total read counts are significantly different among sample groups (p= 4.02e-06 ) based on ANOVA. Total read counts seem to be correlated with factor Gravity (p= 4.73e-08 ). "
# Box plot
x = readData.out$data
boxplot(x, las = 2, col=col1,
ylab='Transformed expression levels',
main='Distribution of transformed data')
#Density plot
par(parDefault)
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
densityPlot()
# Scatter plot of the first two samples
plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2],
main='Scatter plot of first two samples')
####plot gene or gene family
input_selectOrg ="BestMatch"
input_geneSearch <- 'HOXA' #Gene ID for searching
genePlot()
## NULL
input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar?
geneBarPlotError()
## NULL
# hierarchical clustering tree
x <- readData.out$data
maxGene <- apply(x,1,max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1] ) ,]
plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle")
#Correlation matrix
input_labelPCC <- TRUE #Show correlation coefficient?
correlationMatrix()
# Parameters for heatmap
input_nGenes <- 1000 #Top genes for heatmap
input_geneCentering <- TRUE #centering genes ?
input_sampleCentering <- FALSE #Center by sample?
input_geneNormalize <- FALSE #Normalize by gene?
input_sampleNormalize <- FALSE #Normalize by sample?
input_noSampleClustering <- FALSE #Use original sample order
input_heatmapCutoff <- 4 #Remove outliers beyond number of SDs
input_distFunctions <- 1 #which distant funciton to use
input_hclustFunctions <- 1 #Linkage type
input_heatColors1 <- 1 #Colors
input_selectFactorsHeatmap <- 'Gravity' #Sample coloring factors
png('heatmap.png', width = 10, height = 15, units = 'in', res = 300)
staticHeatmap()
dev.off()
## png
## 2
[heatmap] (heatmap.png)
heatmapPlotly() # interactive heatmap using Plotly
input_nGenesKNN <- 2000 #Number of genes fro k-Means
input_nClusters <- 4 #Number of clusters
maxGeneClustering = 12000
input_kmeansNormalization <- 'geneMean' #Normalization
input_KmeansReRun <- 0 #Random seed
distributionSD() #Distribution of standard deviations
KmeansNclusters() #Number of clusters
Kmeans.out = Kmeans() #Running K-means
KmeansHeatmap() #Heatmap for k-Means
#Read gene sets for enrichment analysis
sqlite <- dbDriver('SQLite')
input_selectGO3 <- 'GOBP' #Gene set category
input_minSetSize <- 15 #Min gene set size
input_maxSetSize <- 2000 #Max gene set size
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO3,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
# Alternatively, users can use their own GMT files by
#GeneSets.out <- readGMTRobust('somefile.GMT')
results <- KmeansGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 3.40e-120 | 96 | Photosynthesis |
| 2.11e-65 | 53 | Photosynthesis, light reaction | |
| 4.80e-47 | 63 | Generation of precursor metabolites and energy | |
| 3.88e-35 | 92 | Organonitrogen compound biosynthetic process | |
| 1.81e-32 | 82 | Oxidation-reduction process | |
| 3.80e-29 | 23 | Photosynthetic electron transport chain | |
| 2.12e-28 | 20 | Photosynthesis, light harvesting | |
| 1.08e-27 | 91 | Response to abiotic stimulus | |
| 1.58e-26 | 53 | Response to light stimulus | |
| 5.30e-26 | 35 | Response to cytokinin | |
| B | 1.01e-52 | 75 | Plastid organization |
| 4.02e-51 | 178 | Response to abiotic stimulus | |
| 2.64e-36 | 141 | Small molecule metabolic process | |
| 3.79e-36 | 54 | Chloroplast organization | |
| 1.19e-31 | 119 | Oxidation-reduction process | |
| 4.84e-29 | 120 | Response to oxygen-containing compound | |
| 1.55e-27 | 100 | Organic acid metabolic process | |
| 2.94e-27 | 99 | Oxoacid metabolic process | |
| 5.86e-27 | 132 | Response to organic substance | |
| 2.37e-26 | 94 | Transmembrane transport | |
| C | 8.43e-19 | 41 | Oxidation-reduction process |
| 1.06e-18 | 16 | Antibiotic catabolic process | |
| 1.06e-18 | 19 | Cellular response to toxic substance | |
| 1.06e-18 | 20 | Detoxification | |
| 1.16e-18 | 15 | Hydrogen peroxide catabolic process | |
| 5.36e-18 | 18 | Cellular detoxification | |
| 4.54e-17 | 17 | Cellular oxidant detoxification | |
| 5.09e-17 | 15 | Hydrogen peroxide metabolic process | |
| 2.11e-16 | 21 | Response to toxic substance | |
| 2.96e-16 | 15 | Cofactor catabolic process | |
| D | 7.69e-77 | 134 | Response to inorganic substance |
| 1.16e-68 | 86 | Response to cadmium ion | |
| 1.87e-68 | 181 | Response to abiotic stimulus | |
| 1.27e-67 | 95 | Response to metal ion | |
| 7.98e-57 | 153 | Small molecule metabolic process | |
| 3.75e-55 | 148 | Organonitrogen compound biosynthetic process | |
| 1.15e-51 | 92 | Response to osmotic stress | |
| 5.54e-50 | 86 | Response to salt stress | |
| 5.21e-46 | 113 | Oxoacid metabolic process | |
| 1.17e-45 | 113 | Organic acid metabolic process |
input_seedTSNE <- 0 #Random seed for t-SNE
input_colorGenes <- TRUE #Color genes in t-SNE plot?
tSNEgenePlot() #Plot genes using t-SNE
input_selectFactors <- 'Gravity' #Factor coded by color
input_selectFactors2 <- 'Tissue' #Factor coded by shape
input_tsneSeed2 <- 0 #Random seed for t-SNE
#PCA, MDS and t-SNE plots
PCAplot()
MDSplot()
tSNEplot()
#Read gene sets for pathway analysis using PGSEA on principal components
input_selectGO6 <- 'GOBP'
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO6,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
PCApathway() # Run PGSEA analysis
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
## version 3.12
cat( PCA2factor() ) #The correlation between PCs with factors
##
## Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Gravity (p=2.44e-14).
input_CountsDEGMethod <- 3 #DESeq2= 3,limma-voom=2,limma-trend=1
input_limmaPval <- 0.1 #FDR cutoff
input_limmaFC <- 2 #Fold-change cutoff
input_selectModelComprions <- 'Gravity: Microgravity vs. Terrestrial' #Selected comparisons
input_selectFactorsModel <- 'Gravity' #Selected comparisons
input_selectInteractions <- NULL #Selected comparisons
input_selectBlockFactorsModel <- NULL #Selected comparisons
factorReferenceLevels.out <- c('Gravity:Microgravity')
limma.out <- limma()
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
DEG.data.out <- DEG.data()
limma.out$comparisons
## [1] "Microgravity-Terrestrial"
input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
input_UpDownRegulated <- FALSE #Split up and down regulated genes
vennPlot() # Venn diagram
sigGeneStats() # number of DEGs as figure
sigGeneStatsTable() # number of DEGs as table
## Comparisons Up Down
## Microgravity-Terrestrial Microgravity-Terrestrial 2820 2816
input_selectContrast <- 'Microgravity-Terrestrial' #Selected comparisons
selectedHeatmap.data.out <- selectedHeatmap.data()
selectedHeatmap() # heatmap for DEGs in selected comparison
# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv')
write.csv(DEG.data(),'DEG.data.csv' )
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
input_selectGO2 <- 'GOBP' #Gene set category
geneListData.out <- geneListData()
volcanoPlot()
scatterPlot()
MAplot()
geneListGOTable.out <- geneListGOTable()
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO2,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_removeRedudantSets <- TRUE #Remove highly redundant gene sets?
results <- geneListGO() #Enrichment analysis
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | adj.Pval | nGenes | Pathways |
|---|---|---|---|
| Down regulated | 6.7e-49 | 176 | Cell wall organization or biogenesis |
| 1.6e-48 | 351 | Cell communication | |
| 2.1e-48 | 291 | Oxidation-reduction process | |
| 6.7e-47 | 322 | Signaling | |
| 8.2e-46 | 151 | External encapsulating structure organization | |
| 9.4e-46 | 317 | Signal transduction | |
| 2.0e-43 | 143 | Cell wall organization | |
| 1.4e-40 | 214 | Carbohydrate metabolic process | |
| 3.2e-39 | 225 | Transmembrane transport | |
| 2.9e-36 | 286 | Response to hormone | |
| Up regulated | 5.9e-156 | 189 | Photosynthesis |
| 4.6e-112 | 173 | Plastid organization | |
| 4.3e-99 | 110 | Photosynthesis, light reaction | |
| 4.2e-83 | 131 | Chloroplast organization | |
| 4.6e-81 | 438 | Response to abiotic stimulus | |
| 7.8e-79 | 345 | Oxidation-reduction process | |
| 3.4e-69 | 373 | Small molecule metabolic process | |
| 1.0e-63 | 164 | Generation of precursor metabolites and energy | |
| 3.1e-53 | 191 | Response to light stimulus | |
| 6.0e-51 | 192 | Response to radiation |
STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.
STRING10_species = read.csv(STRING10_speciesFile)
ix = grep('Arabidopsis thaliana', STRING10_species$official_name )
findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
findTaxonomyID.out
## [1] 3702
Enrichment analysis using STRING
STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Warning: we couldn't map to STRING 0% of your identifiers
input_STRINGdbGO <- 'Process' #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro'
results <- stringDB_GO_enrichmentData() # enrichment using STRING
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : methodMT parameter is depecated. Only FDR correction is available.
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : iea parameter is deprecated.
## [1] "Process"
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : methodMT parameter is depecated. Only FDR correction is available.
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : iea parameter is deprecated.
## [1] "Process"
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| “No significant enrichment found.” | adj.Pval |
|---|---|
| No significant enrichment found. | NULL |
PPI network retrieval and analysis
input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis
stringDB_network1(1) #Show PPI network
Generating interactive PPI
write(stringDB_network_link(), 'PPI_results.html') # write results to html file
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
## Warning: we couldn't map to STRING 0% of your identifiers
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
browseURL('PPI_results.html') # open in browser
input_selectContrast1 <- 'Microgravity-Terrestrial' #select Comparison
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'GOBP' #Gene set category
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15 #Min size for gene set
input_maxSetSize <- 2000 #Max size for gene set
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_pathwayPvalCutoff <- 0.2 #FDR cutoff
input_nPathwayShow <- 30 #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1 #FDR to remove genes
input_pathwayMethod = 1 # 1 GAGE
gagePathwayData.out <- gagePathwayData() # pathway analysis using GAGE
results <- gagePathwayData.out #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GAGE analysis: Microgravity vs Terrestrial | statistic | Genes | adj.Pval |
|---|---|---|---|---|
| Down | Root development | -6.3445 | 438 | 1.8e-07 |
| Root system development | -6.3339 | 439 | 1.8e-07 | |
| Root morphogenesis | -5.8486 | 229 | 3.1e-06 | |
| External encapsulating structure organization | -5.1164 | 435 | 9.1e-05 | |
| Cell wall organization | -5.0641 | 412 | 9.6e-05 | |
| Up | Photosynthesis | 15.5205 | 222 | 2.8e-40 |
| Photosynthesis, light reaction | 13.3132 | 119 | 1.1e-27 | |
| Plastid organization | 10.2187 | 256 | 6.4e-20 | |
| Photosynthetic electron transport chain | 10.0483 | 46 | 4.2e-14 | |
| Photosynthesis, light harvesting in photosystem I | 10.0345 | 16 | 3.8e-08 | |
| Photosynthesis, light harvesting | 9.6901 | 31 | 1.1e-11 | |
| Chloroplast organization | 8.5912 | 197 | 4.2e-14 | |
| Generation of precursor metabolites and energy | 8.1922 | 391 | 1.8e-13 | |
| Photosystem II assembly | 7.4063 | 25 | 2.0e-07 | |
| Protein-chromophore linkage | 7.2691 | 39 | 2.5e-08 | |
| Tetrapyrrole metabolic process | 6.9298 | 93 | 8.0e-09 | |
| Chlorophyll metabolic process | 6.9246 | 81 | 9.4e-09 | |
| Porphyrin-containing compound metabolic process | 6.8832 | 92 | 9.4e-09 | |
| Thylakoid membrane organization | 6.8321 | 46 | 1.2e-07 | |
| Chlorophyll biosynthetic process | 6.0264 | 58 | 1.2e-06 | |
| Tetrapyrrole biosynthetic process | 6.0236 | 70 | 9.1e-07 | |
| Response to high light intensity | 5.9608 | 67 | 1.2e-06 | |
| Porphyrin-containing compound biosynthetic process | 5.849 | 67 | 1.9e-06 | |
| Response to light intensity | 5.6113 | 132 | 2.6e-06 | |
| Electron transport chain | 5.5453 | 177 | 2.8e-06 | |
| Cofactor biosynthetic process | 5.384 | 283 | 4.8e-06 | |
| Plastid membrane organization | 5.3788 | 49 | 2.2e-05 | |
| Regulation of photosynthesis | 5.2529 | 37 | 5.7e-05 | |
| NcRNA metabolic process | 4.9137 | 425 | 4.6e-05 | |
| Response to temperature stimulus | 4.7745 | 490 | 7.8e-05 |
pathwayListData.out = pathwayListData()
enrichmentPlot(pathwayListData.out, 25 )
enrichmentNetwork(pathwayListData.out )
enrichmentNetworkPlotly(pathwayListData.out)
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
input_pathwayMethod = 3 # 1 fgsea
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
results <- fgseaPathwayData.out #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GSEA analysis: Microgravity vs Terrestrial | NES | Genes | adj.Pval |
|---|---|---|---|---|
| Up | Photosynthesis | 3.0162 | 222 | 9.2e-03 |
| Photosynthesis, light reaction | 2.9021 | 119 | 7.8e-03 | |
| Photosynthetic electron transport chain | 2.6234 | 46 | 7.2e-03 | |
| Plastid organization | 2.5596 | 256 | 9.9e-03 | |
| Porphyrin-containing compound metabolic process | 2.5016 | 92 | 7.4e-03 | |
| Chlorophyll metabolic process | 2.4886 | 81 | 7.4e-03 | |
| Tetrapyrrole metabolic process | 2.4822 | 93 | 7.4e-03 | |
| Photosynthesis, light harvesting | 2.4743 | 31 | 7.2e-03 | |
| Chlorophyll biosynthetic process | 2.4616 | 58 | 7.3e-03 | |
| Porphyrin-containing compound biosynthetic process | 2.4614 | 67 | 7.4e-03 | |
| Generation of precursor metabolites and energy | 2.4504 | 391 | 1.3e-02 | |
| Chloroplast organization | 2.4447 | 197 | 8.9e-03 | |
| Protein-chromophore linkage | 2.4431 | 39 | 7.2e-03 | |
| Response to high light intensity | 2.4328 | 67 | 7.4e-03 | |
| Tetrapyrrole biosynthetic process | 2.4112 | 70 | 7.4e-03 | |
| Thylakoid membrane organization | 2.3774 | 46 | 7.2e-03 | |
| Electron transport chain | 2.3751 | 177 | 8.5e-03 | |
| Photosystem II assembly | 2.2991 | 25 | 7.2e-03 | |
| Regulation of photosynthesis | 2.2822 | 37 | 7.2e-03 | |
| Response to light intensity | 2.2579 | 132 | 7.9e-03 | |
| Translation | 2.2516 | 617 | 2.4e-02 | |
| Protein peptidyl-prolyl isomerization | 2.2495 | 55 | 7.3e-03 | |
| Peptide biosynthetic process | 2.2467 | 620 | 2.4e-02 | |
| Regulation of generation of precursor metabolites and energy | 2.1869 | 27 | 7.2e-03 | |
| Plastid membrane organization | 2.1865 | 49 | 7.2e-03 | |
| Photosynthesis, light harvesting in photosystem I | 2.1745 | 16 | 7.2e-03 | |
| Protein targeting to chloroplast | 2.1741 | 43 | 7.2e-03 | |
| Establishment of protein localization to chloroplast | 2.1741 | 43 | 7.2e-03 | |
| Protein localization to chloroplast | 2.173 | 45 | 7.2e-03 | |
| Amide biosynthetic process | 2.1635 | 681 | 2.6e-02 |
pathwayListData.out = pathwayListData()
enrichmentPlot(pathwayListData.out, 25 )
enrichmentNetwork(pathwayListData.out )
enrichmentNetworkPlotly(pathwayListData.out)
PGSEAplot() # pathway analysis using PGSEA
##
## Computing P values using ANOVA
input_selectContrast2 <- 'Microgravity-Terrestrial' #select Comparison
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1 #FDR to filter genes
input_limmaFCViz <- 2 #FDR to filter genes
genomePlotly() # shows fold-changes on the genome
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion
input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ...
biclustering.out = biclustering() # run analysis
input_selectBicluster <- 1 #select a cluster
biclustHeatmap() # heatmap for selected cluster
input_selectGO4 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO4,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
results <- geneListBclustGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 3.7e-74 | 199 | Response to abiotic stimulus |
| 4.3e-59 | 162 | Organonitrogen compound biosynthetic process |
| 1.1e-56 | 119 | Response to inorganic substance |
| 2.8e-47 | 149 | Small molecule metabolic process |
| 4.7e-41 | 84 | Cofactor metabolic process |
| 2.5e-40 | 125 | Oxidation-reduction process |
| 6.0e-40 | 61 | Plastid organization |
| 9.6e-40 | 73 | Response to metal ion |
| 2.6e-39 | 64 | Response to cadmium ion |
| 4.0e-37 | 141 | Response to organic substance |
input_mySoftPower <- 5 #SoftPower to cutoff
input_nGenesNetwork <- 1000 #Number of top genes
input_minModuleSize <- 20 #Module size minimum
wgcna.out = wgcna() # run WGCNA
## Warning: executing %dopar% sequentially: no parallel backend registered
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.5090 1.8500 0.770 880 930 937
## 2 2 0.3240 0.9140 0.742 814 887 900
## 3 3 0.2380 0.6800 0.768 766 854 872
## 4 4 0.1630 0.4900 0.725 728 826 850
## 5 5 0.1270 0.4040 0.723 697 802 831
## 6 6 0.1050 0.3650 0.784 670 781 814
## 7 7 0.0852 0.2990 0.766 647 761 800
## 8 8 0.0996 0.3450 0.862 626 743 786
## 9 9 0.0683 0.2750 0.851 607 727 774
## 10 10 0.0590 0.2380 0.831 590 711 763
## 11 12 0.0498 0.2110 0.858 560 683 743
## 12 14 0.0360 0.1710 0.837 534 657 725
## 13 16 0.0213 0.1160 0.814 511 633 708
## 14 18 0.0203 0.1130 0.827 491 612 693
## 15 20 0.0127 0.0871 0.808 472 592 680
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
softPower() # soft power curve
modulePlot() # plot modules
listWGCNA.Modules.out = listWGCNA.Modules() #modules
input_selectGO5 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO5,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_selectWGCNA.Module <- '1. turquoise (999 genes)' #Select a module
input_topGenesNetwork <- 10 #SoftPower to cutoff
input_edgeThreshold <- 0.4 #Number of top genes
moduleNetwork() # show network of top genes in selected module
## softConnectivity: FYI: connecitivty of genes with less than 4 valid samples will be returned as NA.
## ..calculating connectivities..
input_removeRedudantSets <- TRUE #Remove redundant gene sets
results <- networkModuleGO() #Enrichment analysis of selected module
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.2e-115 | 121 | Photosynthesis |
| 7.5e-114 | 284 | Response to abiotic stimulus |
| 1.0e-78 | 200 | Oxidation-reduction process |
| 5.5e-77 | 160 | Response to inorganic substance |
| 3.1e-75 | 216 | Small molecule metabolic process |
| 1.1e-64 | 122 | Cofactor metabolic process |
| 2.9e-63 | 106 | Generation of precursor metabolites and energy |
| 5.4e-62 | 194 | Organonitrogen compound biosynthetic process |
| 1.6e-58 | 63 | Photosynthesis, light reaction |
| 2.9e-56 | 77 | Response to cytokinin |